1.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "63372ade",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17c972dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import *\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import *\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a45540",
   "metadata": {},
   "source": [
    "# Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "458cb2d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'CRC/'\n",
    "df = pd.read_csv(path + \"/all_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3dd0d308",
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl=LabelEncoder()\n",
    "df['condition'] = lbl.fit_transform(df['condition'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57ef2350",
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = df[\"Country\"].to_numpy()\n",
    "X = df.drop([\"condition\",\"Unnamed: 0\", \"Country\"], axis=1)\n",
    "y = df[\"condition\"].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1fe4d4bc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Streptococcus_anginosus___ref_mOTU_v2_0004__</th>\n",
       "      <th>Enterobacteriaceae_sp.___ref_mOTU_v2_0036__</th>\n",
       "      <th>Citrobacter_sp.___ref_mOTU_v2_0076__</th>\n",
       "      <th>Klebsiella_michiganensis/oxytoca___ref_mOTU_v2_0079__</th>\n",
       "      <th>Enterococcus_faecalis___ref_mOTU_v2_0116__</th>\n",
       "      <th>Lactobacillus_salivarius___ref_mOTU_v2_0125__</th>\n",
       "      <th>Dielma_fastidiosa___ref_mOTU_v2_0138__</th>\n",
       "      <th>Streptococcus_constellatus/intermedius___ref_mOTU_v2_0143__</th>\n",
       "      <th>Streptococcus_parasanguinis___ref_mOTU_v2_0144__</th>\n",
       "      <th>Streptococcus_sp._HSISM1___ref_mOTU_v2_0145__</th>\n",
       "      <th>...</th>\n",
       "      <th>unknown_Porphyromonas___meta_mOTU_v2_7777__</th>\n",
       "      <th>unknown_Clostridiales___meta_mOTU_v2_7778__</th>\n",
       "      <th>unknown_Clostridiales___meta_mOTU_v2_7781__</th>\n",
       "      <th>unknown_Clostridiales___meta_mOTU_v2_7782__</th>\n",
       "      <th>unknown_Clostridiales___meta_mOTU_v2_7784__</th>\n",
       "      <th>Clostridium_sp._CAG__230___meta_mOTU_v2_7788__</th>\n",
       "      <th>Clostridium_sp._CAG__1193___meta_mOTU_v2_7789__</th>\n",
       "      <th>unknown_Erysipelotrichaceae___meta_mOTU_v2_7790__</th>\n",
       "      <th>unknown_Clostridiales___meta_mOTU_v2_7795__</th>\n",
       "      <th>unknown_Clostridiales___meta_mOTU_v2_7800__</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 849 columns</p>\n",
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      ],
      "text/plain": [
       "     Streptococcus_anginosus___ref_mOTU_v2_0004__  \\\n",
       "0                                        0.000000   \n",
       "1                                        0.000591   \n",
       "2                                        0.000840   \n",
       "3                                        0.000000   \n",
       "4                                        0.000086   \n",
       "..                                            ...   \n",
       "763                                      0.000000   \n",
       "764                                      0.000038   \n",
       "765                                      0.000000   \n",
       "766                                      0.000000   \n",
       "767                                      0.000000   \n",
       "\n",
       "     Enterobacteriaceae_sp.___ref_mOTU_v2_0036__  \\\n",
       "0                                       0.000000   \n",
       "1                                       0.000066   \n",
       "2                                       0.000065   \n",
       "3                                       0.000071   \n",
       "4                                       0.019236   \n",
       "..                                           ...   \n",
       "763                                     0.000118   \n",
       "764                                     0.000038   \n",
       "765                                     0.000000   \n",
       "766                                     0.000312   \n",
       "767                                     0.000000   \n",
       "\n",
       "     Citrobacter_sp.___ref_mOTU_v2_0076__  \\\n",
       "0                                0.000000   \n",
       "1                                0.000000   \n",
       "2                                0.002454   \n",
       "3                                0.000000   \n",
       "4                                0.001111   \n",
       "..                                    ...   \n",
       "763                              0.000000   \n",
       "764                              0.000000   \n",
       "765                              0.000000   \n",
       "766                              0.000000   \n",
       "767                              0.000784   \n",
       "\n",
       "     Klebsiella_michiganensis/oxytoca___ref_mOTU_v2_0079__  \\\n",
       "0                                             0.000000       \n",
       "1                                             0.000000       \n",
       "2                                             0.000000       \n",
       "3                                             0.000000       \n",
       "4                                             0.000256       \n",
       "..                                                 ...       \n",
       "763                                           0.000000       \n",
       "764                                           0.000000       \n",
       "765                                           0.000000       \n",
       "766                                           0.000000       \n",
       "767                                           0.000000       \n",
       "\n",
       "     Enterococcus_faecalis___ref_mOTU_v2_0116__  \\\n",
       "0                                      0.000000   \n",
       "1                                      0.000000   \n",
       "2                                      0.000065   \n",
       "3                                      0.000000   \n",
       "4                                      0.000086   \n",
       "..                                          ...   \n",
       "763                                    0.000059   \n",
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       "765                                    0.000000   \n",
       "766                                    0.000208   \n",
       "767                                    0.000000   \n",
       "\n",
       "     Lactobacillus_salivarius___ref_mOTU_v2_0125__  \\\n",
       "0                                         0.000000   \n",
       "1                                         0.000000   \n",
       "2                                         0.013111   \n",
       "3                                         0.000000   \n",
       "4                                         0.000086   \n",
       "..                                             ...   \n",
       "763                                       0.000000   \n",
       "764                                       0.000000   \n",
       "765                                       0.000000   \n",
       "766                                       0.000000   \n",
       "767                                       0.000000   \n",
       "\n",
       "     Dielma_fastidiosa___ref_mOTU_v2_0138__  \\\n",
       "0                                  0.000083   \n",
       "1                                  0.000000   \n",
       "2                                  0.000000   \n",
       "3                                  0.000213   \n",
       "4                                  0.000000   \n",
       "..                                      ...   \n",
       "763                                0.000118   \n",
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       "765                                0.000000   \n",
       "766                                0.000000   \n",
       "767                                0.000000   \n",
       "\n",
       "     Streptococcus_constellatus/intermedius___ref_mOTU_v2_0143__  \\\n",
       "0                                             0.000000             \n",
       "1                                             0.000066             \n",
       "2                                             0.000065             \n",
       "3                                             0.000071             \n",
       "4                                             0.000000             \n",
       "..                                                 ...             \n",
       "763                                           0.000000             \n",
       "764                                           0.000038             \n",
       "765                                           0.000000             \n",
       "766                                           0.000000             \n",
       "767                                           0.000000             \n",
       "\n",
       "     Streptococcus_parasanguinis___ref_mOTU_v2_0144__  \\\n",
       "0                                            0.000249   \n",
       "1                                            0.002102   \n",
       "2                                            0.012013   \n",
       "3                                            0.000142   \n",
       "4                                            0.000513   \n",
       "..                                                ...   \n",
       "763                                          0.000059   \n",
       "764                                          0.000113   \n",
       "765                                          0.000000   \n",
       "766                                          0.000000   \n",
       "767                                          0.000087   \n",
       "\n",
       "     Streptococcus_sp._HSISM1___ref_mOTU_v2_0145__  ...  \\\n",
       "0                                         0.000249  ...   \n",
       "1                                         0.000197  ...   \n",
       "2                                         0.002777  ...   \n",
       "3                                         0.000000  ...   \n",
       "4                                         0.000000  ...   \n",
       "..                                             ...  ...   \n",
       "763                                       0.000000  ...   \n",
       "764                                       0.000113  ...   \n",
       "765                                       0.000000  ...   \n",
       "766                                       0.000000  ...   \n",
       "767                                       0.000087  ...   \n",
       "\n",
       "     unknown_Porphyromonas___meta_mOTU_v2_7777__  \\\n",
       "0                                       0.000000   \n",
       "1                                       0.000000   \n",
       "2                                       0.000000   \n",
       "3                                       0.000000   \n",
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       "..                                           ...   \n",
       "763                                     0.002590   \n",
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       "766                                     0.000000   \n",
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       "\n",
       "     unknown_Clostridiales___meta_mOTU_v2_7778__  \\\n",
       "0                                       0.000000   \n",
       "1                                       0.000000   \n",
       "2                                       0.000000   \n",
       "3                                       0.000000   \n",
       "4                                       0.000171   \n",
       "..                                           ...   \n",
       "763                                     0.000000   \n",
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       "765                                     0.000348   \n",
       "766                                     0.000000   \n",
       "767                                     0.000000   \n",
       "\n",
       "     unknown_Clostridiales___meta_mOTU_v2_7781__  \\\n",
       "0                                       0.000000   \n",
       "1                                       0.000000   \n",
       "2                                       0.000194   \n",
       "3                                       0.000000   \n",
       "4                                       0.000000   \n",
       "..                                           ...   \n",
       "763                                     0.000000   \n",
       "764                                     0.000000   \n",
       "765                                     0.000044   \n",
       "766                                     0.000000   \n",
       "767                                     0.000000   \n",
       "\n",
       "     unknown_Clostridiales___meta_mOTU_v2_7782__  \\\n",
       "0                                       0.000000   \n",
       "1                                       0.000788   \n",
       "2                                       0.000065   \n",
       "3                                       0.000000   \n",
       "4                                       0.000000   \n",
       "..                                           ...   \n",
       "763                                     0.001118   \n",
       "764                                     0.000151   \n",
       "765                                     0.001741   \n",
       "766                                     0.000000   \n",
       "767                                     0.000000   \n",
       "\n",
       "     unknown_Clostridiales___meta_mOTU_v2_7784__  \\\n",
       "0                                       0.000000   \n",
       "1                                       0.000000   \n",
       "2                                       0.000000   \n",
       "3                                       0.000000   \n",
       "4                                       0.000000   \n",
       "..                                           ...   \n",
       "763                                     0.000000   \n",
       "764                                     0.000038   \n",
       "765                                     0.000000   \n",
       "766                                     0.000208   \n",
       "767                                     0.000000   \n",
       "\n",
       "     Clostridium_sp._CAG__230___meta_mOTU_v2_7788__  \\\n",
       "0                                          0.000000   \n",
       "1                                          0.000000   \n",
       "2                                          0.000840   \n",
       "3                                          0.000000   \n",
       "4                                          0.000000   \n",
       "..                                              ...   \n",
       "763                                        0.000000   \n",
       "764                                        0.000000   \n",
       "765                                        0.000435   \n",
       "766                                        0.000000   \n",
       "767                                        0.000000   \n",
       "\n",
       "     Clostridium_sp._CAG__1193___meta_mOTU_v2_7789__  \\\n",
       "0                                                0.0   \n",
       "1                                                0.0   \n",
       "2                                                0.0   \n",
       "3                                                0.0   \n",
       "4                                                0.0   \n",
       "..                                               ...   \n",
       "763                                              0.0   \n",
       "764                                              0.0   \n",
       "765                                              0.0   \n",
       "766                                              0.0   \n",
       "767                                              0.0   \n",
       "\n",
       "     unknown_Erysipelotrichaceae___meta_mOTU_v2_7790__  \\\n",
       "0                                                  0.0   \n",
       "1                                                  0.0   \n",
       "2                                                  0.0   \n",
       "3                                                  0.0   \n",
       "4                                                  0.0   \n",
       "..                                                 ...   \n",
       "763                                                0.0   \n",
       "764                                                0.0   \n",
       "765                                                0.0   \n",
       "766                                                0.0   \n",
       "767                                                0.0   \n",
       "\n",
       "     unknown_Clostridiales___meta_mOTU_v2_7795__  \\\n",
       "0                                       0.000083   \n",
       "1                                       0.000000   \n",
       "2                                       0.001808   \n",
       "3                                       0.000000   \n",
       "4                                       0.000000   \n",
       "..                                           ...   \n",
       "763                                     0.000000   \n",
       "764                                     0.000000   \n",
       "765                                     0.000000   \n",
       "766                                     0.000000   \n",
       "767                                     0.000000   \n",
       "\n",
       "     unknown_Clostridiales___meta_mOTU_v2_7800__  \n",
       "0                                       0.000083  \n",
       "1                                       0.000000  \n",
       "2                                       0.000000  \n",
       "3                                       0.000000  \n",
       "4                                       0.000000  \n",
       "..                                           ...  \n",
       "763                                     0.000000  \n",
       "764                                     0.000000  \n",
       "765                                     0.000044  \n",
       "766                                     0.000156  \n",
       "767                                     0.000000  \n",
       "\n",
       "[768 rows x 849 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874330dc",
   "metadata": {},
   "source": [
    "## Dataset names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d31b47b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AT-CRC_species',\n",
       " 'CN-CRC_species',\n",
       " 'DE-CRC_species',\n",
       " 'FR-CRC_species',\n",
       " 'IT-CRC-2_species',\n",
       " 'IT-CRC_species',\n",
       " 'JP-CRC_species',\n",
       " 'US-CRC_species']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_groups = list(np.unique(groups))\n",
    "unique_groups"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a331e388",
   "metadata": {},
   "source": [
    "# Creating dataframes for results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be80a857",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "AUC_scores = pd.DataFrame(index=unique_groups, columns=unique_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "21e98d1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ROC_curve = pd.DataFrame(index=unique_groups, columns=[\"fpr\",\"tpr\",\"auc\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ec21a3",
   "metadata": {},
   "source": [
    "# LOSO validation with random forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82e26546",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 123\n",
    "show_fold_stats = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44d0a12c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "Fold : 1 CN-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.9223\n",
      "F1 : 0.8000, Recall : 0.8519 , Precision : 0.7541\n",
      "[[59 15]\n",
      " [ 8 46]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 2 DE-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8306\n",
      "F1 : 0.7761, Recall : 0.8667 , Precision : 0.7027\n",
      "[[38 22]\n",
      " [ 8 52]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 3 FR-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8597\n",
      "F1 : 0.7207, Recall : 0.6557 , Precision : 0.8000\n",
      "[[43 10]\n",
      " [21 40]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 4 IT-CRC-2_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7762\n",
      "F1 : 0.6984, Recall : 0.7857 , Precision : 0.6286\n",
      "[[19 13]\n",
      " [ 6 22]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 5 IT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7579\n",
      "F1 : 0.6047, Recall : 0.5417 , Precision : 0.6842\n",
      "[[23  6]\n",
      " [11 13]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 6 JP-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8281\n",
      "F1 : 0.5806, Recall : 0.4500 , Precision : 0.8182\n",
      "[[36  4]\n",
      " [22 18]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 7 US-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7607\n",
      "F1 : 0.6667, Recall : 0.6538 , Precision : 0.6800\n",
      "[[36 16]\n",
      " [18 34]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Group : 1 AT-CRC_species\n",
      "ROC AUC score for RandomForest model, LOGO test set: 0.8837\n",
      "F1 : 0.8657, Recall : 0.9206 , Precision : 0.8169\n",
      "[[33 13]\n",
      " [ 5 58]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 1 AT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8899\n",
      "F1 : 0.8593, Recall : 0.9206 , Precision : 0.8056\n",
      "[[32 14]\n",
      " [ 5 58]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 2 DE-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8231\n",
      "F1 : 0.7344, Recall : 0.7833 , Precision : 0.6912\n",
      "[[39 21]\n",
      " [13 47]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 3 FR-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8508\n",
      "F1 : 0.7692, Recall : 0.7377 , Precision : 0.8036\n",
      "[[42 11]\n",
      " [16 45]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 4 IT-CRC-2_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8147\n",
      "F1 : 0.7761, Recall : 0.9286 , Precision : 0.6667\n",
      "[[19 13]\n",
      " [ 2 26]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 5 IT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7076\n",
      "F1 : 0.5714, Recall : 0.5000 , Precision : 0.6667\n",
      "[[23  6]\n",
      " [12 12]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 6 JP-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8653\n",
      "F1 : 0.8056, Recall : 0.7250 , Precision : 0.9062\n",
      "[[37  3]\n",
      " [11 29]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 7 US-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7633\n",
      "F1 : 0.7027, Recall : 0.7500 , Precision : 0.6610\n",
      "[[32 20]\n",
      " [13 39]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Group : 2 CN-CRC_species\n",
      "ROC AUC score for RandomForest model, LOGO test set: 0.9277\n",
      "F1 : 0.8235, Recall : 0.9074 , Precision : 0.7538\n",
      "[[58 16]\n",
      " [ 5 49]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 1 AT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8490\n",
      "F1 : 0.8551, Recall : 0.9365 , Precision : 0.7867\n",
      "[[30 16]\n",
      " [ 4 59]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 2 CN-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.9301\n",
      "F1 : 0.7833, Recall : 0.8704 , Precision : 0.7121\n",
      "[[55 19]\n",
      " [ 7 47]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 3 FR-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8470\n",
      "F1 : 0.7769, Recall : 0.7705 , Precision : 0.7833\n",
      "[[40 13]\n",
      " [14 47]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 4 IT-CRC-2_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8142\n",
      "F1 : 0.7463, Recall : 0.8929 , Precision : 0.6410\n",
      "[[18 14]\n",
      " [ 3 25]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 5 IT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7615\n",
      "F1 : 0.5500, Recall : 0.4583 , Precision : 0.6875\n",
      "[[24  5]\n",
      " [13 11]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 6 JP-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8803\n",
      "F1 : 0.7077, Recall : 0.5750 , Precision : 0.9200\n",
      "[[38  2]\n",
      " [17 23]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 7 US-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7592\n",
      "F1 : 0.6538, Recall : 0.6538 , Precision : 0.6538\n",
      "[[34 18]\n",
      " [18 34]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Group : 3 DE-CRC_species\n",
      "ROC AUC score for RandomForest model, LOGO test set: 0.8307\n",
      "F1 : 0.7597, Recall : 0.8167 , Precision : 0.7101\n",
      "[[40 20]\n",
      " [11 49]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 1 AT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8756\n",
      "F1 : 0.8387, Recall : 0.8254 , Precision : 0.8525\n",
      "[[37  9]\n",
      " [11 52]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 2 CN-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.9371\n",
      "F1 : 0.8305, Recall : 0.9074 , Precision : 0.7656\n",
      "[[59 15]\n",
      " [ 5 49]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 3 DE-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8299\n",
      "F1 : 0.7520, Recall : 0.7833 , Precision : 0.7231\n",
      "[[42 18]\n",
      " [13 47]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 4 IT-CRC-2_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7991\n",
      "F1 : 0.7097, Recall : 0.7857 , Precision : 0.6471\n",
      "[[20 12]\n",
      " [ 6 22]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 5 IT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7802\n",
      "F1 : 0.6939, Recall : 0.7083 , Precision : 0.6800\n",
      "[[21  8]\n",
      " [ 7 17]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 6 JP-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8897\n",
      "F1 : 0.7761, Recall : 0.6500 , Precision : 0.9630\n",
      "[[39  1]\n",
      " [14 26]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 7 US-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7648\n",
      "F1 : 0.6465, Recall : 0.6154 , Precision : 0.6809\n",
      "[[37 15]\n",
      " [20 32]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Group : 4 FR-CRC_species\n",
      "ROC AUC score for RandomForest model, LOGO test set: 0.8651\n",
      "F1 : 0.7759, Recall : 0.7377 , Precision : 0.8182\n",
      "[[43 10]\n",
      " [16 45]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "Fold : 1 AT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8722\n",
      "F1 : 0.8421, Recall : 0.8889 , Precision : 0.8000\n",
      "[[32 14]\n",
      " [ 7 56]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 2 CN-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.9344\n",
      "F1 : 0.8130, Recall : 0.9259 , Precision : 0.7246\n",
      "[[55 19]\n",
      " [ 4 50]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 3 DE-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8421\n",
      "F1 : 0.7969, Recall : 0.8500 , Precision : 0.7500\n",
      "[[43 17]\n",
      " [ 9 51]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 4 FR-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8611\n",
      "F1 : 0.7692, Recall : 0.7377 , Precision : 0.8036\n",
      "[[42 11]\n",
      " [16 45]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 5 IT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7407\n",
      "F1 : 0.5714, Recall : 0.5000 , Precision : 0.6667\n",
      "[[23  6]\n",
      " [12 12]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 6 JP-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8794\n",
      "F1 : 0.7188, Recall : 0.5750 , Precision : 0.9583\n",
      "[[39  1]\n",
      " [17 23]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 7 US-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7594\n",
      "F1 : 0.6981, Recall : 0.7115 , Precision : 0.6852\n",
      "[[35 17]\n",
      " [15 37]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Group : 5 IT-CRC-2_species\n",
      "ROC AUC score for RandomForest model, LOGO test set: 0.8036\n",
      "F1 : 0.7500, Recall : 0.8571 , Precision : 0.6667\n",
      "[[20 12]\n",
      " [ 4 24]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 1 AT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8830\n",
      "F1 : 0.8571, Recall : 0.9048 , Precision : 0.8143\n",
      "[[33 13]\n",
      " [ 6 57]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 2 CN-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.9278\n",
      "F1 : 0.8099, Recall : 0.9074 , Precision : 0.7313\n",
      "[[56 18]\n",
      " [ 5 49]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 3 DE-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8425\n",
      "F1 : 0.7812, Recall : 0.8333 , Precision : 0.7353\n",
      "[[42 18]\n",
      " [10 50]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 4 FR-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8603\n",
      "F1 : 0.7759, Recall : 0.7377 , Precision : 0.8182\n",
      "[[43 10]\n",
      " [16 45]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 5 IT-CRC-2_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7946\n",
      "F1 : 0.6774, Recall : 0.7500 , Precision : 0.6176\n",
      "[[19 13]\n",
      " [ 7 21]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 6 JP-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8656\n",
      "F1 : 0.6563, Recall : 0.5250 , Precision : 0.8750\n",
      "[[37  3]\n",
      " [19 21]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 7 US-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7846\n",
      "F1 : 0.7290, Recall : 0.7500 , Precision : 0.7091\n",
      "[[36 16]\n",
      " [13 39]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Group : 6 IT-CRC_species\n",
      "ROC AUC score for RandomForest model, LOGO test set: 0.7076\n",
      "F1 : 0.6047, Recall : 0.5417 , Precision : 0.6842\n",
      "[[23  6]\n",
      " [11 13]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 1 AT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8514\n",
      "F1 : 0.8333, Recall : 0.8730 , Precision : 0.7971\n",
      "[[32 14]\n",
      " [ 8 55]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 2 CN-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.9392\n",
      "F1 : 0.8649, Recall : 0.8889 , Precision : 0.8421\n",
      "[[65  9]\n",
      " [ 6 48]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 3 DE-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8657\n",
      "F1 : 0.7969, Recall : 0.8500 , Precision : 0.7500\n",
      "[[43 17]\n",
      " [ 9 51]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 4 FR-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8653\n",
      "F1 : 0.7788, Recall : 0.7213 , Precision : 0.8462\n",
      "[[45  8]\n",
      " [17 44]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 5 IT-CRC-2_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7885\n",
      "F1 : 0.7385, Recall : 0.8571 , Precision : 0.6486\n",
      "[[19 13]\n",
      " [ 4 24]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 6 IT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7033\n",
      "F1 : 0.5366, Recall : 0.4583 , Precision : 0.6471\n",
      "[[23  6]\n",
      " [13 11]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 7 US-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7709\n",
      "F1 : 0.6327, Recall : 0.5962 , Precision : 0.6739\n",
      "[[37 15]\n",
      " [21 31]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Group : 7 JP-CRC_species\n",
      "ROC AUC score for RandomForest model, LOGO test set: 0.8694\n",
      "F1 : 0.6970, Recall : 0.5750 , Precision : 0.8846\n",
      "[[37  3]\n",
      " [17 23]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 1 AT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8761\n",
      "F1 : 0.8615, Recall : 0.8889 , Precision : 0.8358\n",
      "[[35 11]\n",
      " [ 7 56]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 2 CN-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.9351\n",
      "F1 : 0.8197, Recall : 0.9259 , Precision : 0.7353\n",
      "[[56 18]\n",
      " [ 4 50]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 3 DE-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8282\n",
      "F1 : 0.7360, Recall : 0.7667 , Precision : 0.7077\n",
      "[[41 19]\n",
      " [14 46]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 4 FR-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8681\n",
      "F1 : 0.7611, Recall : 0.7049 , Precision : 0.8269\n",
      "[[44  9]\n",
      " [18 43]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 5 IT-CRC-2_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7840\n",
      "F1 : 0.6984, Recall : 0.7857 , Precision : 0.6286\n",
      "[[19 13]\n",
      " [ 6 22]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 6 IT-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.7320\n",
      "F1 : 0.5714, Recall : 0.5000 , Precision : 0.6667\n",
      "[[23  6]\n",
      " [12 12]]\n",
      "--------------------------------------------------------------------------------\n",
      "Fold : 7 JP-CRC_species\n",
      "ROC AUC score for RandomForest model, validation set: 0.8744\n",
      "F1 : 0.7273, Recall : 0.6000 , Precision : 0.9231\n",
      "[[38  2]\n",
      " [16 24]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "Group : 8 US-CRC_species\n",
      "ROC AUC score for RandomForest model, LOGO test set: 0.7652\n",
      "F1 : 0.6667, Recall : 0.6346 , Precision : 0.7021\n",
      "[[38 14]\n",
      " [19 33]]\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "grp = 0\n",
    "for i in range(len(unique_groups)):\n",
    "    outer_test_index = list(np.where(df[\"Country\"]==unique_groups[i])[0])\n",
    "    \n",
    "    inner_group_list = []\n",
    "    inner_group_list.append(unique_groups[:i] + unique_groups[i+1:])\n",
    "    \n",
    "    outer_train_index = list(np.where(df[\"Country\"]!=unique_groups[i])[0])\n",
    "\n",
    "    #Training dataset\n",
    "    outer_train_X = X.iloc[outer_train_index]\n",
    "    outer_train_y = y[outer_train_index]\n",
    "    \n",
    "    #Test dataset\n",
    "    outer_test_X =  X.iloc[outer_test_index]\n",
    "    outer_test_y =  y[outer_test_index]\n",
    "    \n",
    "    for k,name in enumerate(inner_group_list[0]):\n",
    "        inner_val_index = list(np.where(df[\"Country\"]==name)[0])\n",
    "        \n",
    "        inner_train_names = []\n",
    "        inner_train_names.append(inner_group_list[:k] + inner_group_list[k+1:])\n",
    "        \n",
    "        inner_train_index = list(np.where((df[\"Country\"]!=name) & (df[\"Country\"]!=unique_groups[i]))[0])\n",
    "\n",
    "    \n",
    "        #Inner training dataset\n",
    "        inner_train_X = X.iloc[inner_train_index]\n",
    "        inner_train_y = y[inner_train_index]\n",
    "        \n",
    "        #Inner validation dataset\n",
    "        inner_val_X =  X.iloc[inner_val_index]\n",
    "        inner_val_y =  y[inner_val_index]\n",
    "                \n",
    "        \n",
    "        # Random Forest\n",
    "        clf = RandomForestClassifier(n_estimators = 500, random_state = SEED, verbose = 0, bootstrap=True)\n",
    "        clf.fit(inner_train_X, inner_train_y)\n",
    "\n",
    "        RF_pred_class = clf.predict(inner_val_X)\n",
    "        RF_preds = clf.predict_proba(inner_val_X)\n",
    "\n",
    "        RF_AUC_test_score = roc_auc_score(inner_val_y, RF_preds[:,1])\n",
    "        RF_f1_test = f1_score(inner_val_y, RF_pred_class)\n",
    "        RF_recall_test = recall_score(inner_val_y, RF_pred_class)\n",
    "        RF_precision_test = precision_score(inner_val_y, RF_pred_class)\n",
    "        \n",
    "        #Specificity and sensitivity\n",
    "        tn, fp, fn, tp = confusion_matrix(inner_val_y, RF_pred_class).ravel()\n",
    "        specificity = tn / (tn + fp)\n",
    "        sensitivity = tp / (tp + fn)\n",
    "        \n",
    "        #Adding auc to dataframe\n",
    "        AUC_scores.at[unique_groups[i], name] = RF_AUC_test_score\n",
    "\n",
    "        if show_fold_stats:\n",
    "            print('-' * 80)\n",
    "            print('Fold : %s'%(k+1),name)\n",
    "            print('ROC AUC score for RandomForest model, validation set: %.4f'%RF_AUC_test_score)\n",
    "            print('F1 : %.4f, Recall : %.4f , Precision : %.4f'%(RF_f1_test, RF_recall_test, RF_precision_test))\n",
    "            print(confusion_matrix(inner_val_y, RF_pred_class))\n",
    "                \n",
    "    # Random Forest on logo_test data\n",
    "    clf = RandomForestClassifier(n_estimators = 500, random_state = SEED, verbose = 0, bootstrap=True)\n",
    "    clf.fit(outer_train_X, outer_train_y)\n",
    "        \n",
    "    RF_pred_class = clf.predict(outer_test_X)\n",
    "    RF_preds = clf.predict_proba(outer_test_X)\n",
    "\n",
    "    RF_AUC_test_score = roc_auc_score(outer_test_y, RF_preds[:,1])\n",
    "    RF_f1_test = f1_score(outer_test_y, RF_pred_class)\n",
    "    RF_recall_test = recall_score(outer_test_y, RF_pred_class)\n",
    "    RF_precision_test = precision_score(outer_test_y, RF_pred_class)\n",
    "    \n",
    "    #Specificity and sensitivity\n",
    "    tn, fp, fn, tp = confusion_matrix(outer_test_y, RF_pred_class).ravel()\n",
    "    specificity = tn / (tn + fp)\n",
    "    sensitivity = tp / (tp + fn)\n",
    "    \n",
    "    #Adding auc to dataframe\n",
    "    AUC_scores.at[unique_groups[i], unique_groups[i]] = RF_AUC_test_score\n",
    "    \n",
    "    #Getting AUCROC data\n",
    "    fpr, tpr, _ = roc_curve(outer_test_y,  RF_preds[:,1])\n",
    "\n",
    "    #Adding roc curve values to dataframe\n",
    "    ROC_curve.at[unique_groups[i], \"fpr\"] = \",\".join(str(x) for x in fpr)\n",
    "    ROC_curve.at[unique_groups[i], \"tpr\"] = \",\".join(str(x) for x in tpr)\n",
    "    ROC_curve.at[unique_groups[i], \"auc\"] = RF_AUC_test_score\n",
    "    \n",
    "\n",
    "    if show_fold_stats:\n",
    "        print('-' * 80)\n",
    "        print('-' * 80)\n",
    "        print('Group : %s'%(grp+1),unique_groups[i])\n",
    "        print('ROC AUC score for RandomForest model, LOGO test set: %.4f'%RF_AUC_test_score)\n",
    "        print('F1 : %.4f, Recall : %.4f , Precision : %.4f'%(RF_f1_test, RF_recall_test, RF_precision_test))\n",
    "        print(confusion_matrix(outer_test_y, RF_pred_class))\n",
    "        print('-' * 80)\n",
    "        print('-' * 80)\n",
    "    grp += 1\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48727aee",
   "metadata": {},
   "source": [
    "# Results and plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "401268e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AT-CRC_species</th>\n",
       "      <th>CN-CRC_species</th>\n",
       "      <th>DE-CRC_species</th>\n",
       "      <th>FR-CRC_species</th>\n",
       "      <th>IT-CRC-2_species</th>\n",
       "      <th>IT-CRC_species</th>\n",
       "      <th>JP-CRC_species</th>\n",
       "      <th>US-CRC_species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AT-CRC_species</th>\n",
       "      <td>0.883713</td>\n",
       "      <td>0.922297</td>\n",
       "      <td>0.830556</td>\n",
       "      <td>0.859728</td>\n",
       "      <td>0.776228</td>\n",
       "      <td>0.757902</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>0.760725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN-CRC_species</th>\n",
       "      <td>0.889924</td>\n",
       "      <td>0.927678</td>\n",
       "      <td>0.823056</td>\n",
       "      <td>0.850758</td>\n",
       "      <td>0.814732</td>\n",
       "      <td>0.707615</td>\n",
       "      <td>0.865313</td>\n",
       "      <td>0.763314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE-CRC_species</th>\n",
       "      <td>0.849034</td>\n",
       "      <td>0.930055</td>\n",
       "      <td>0.830694</td>\n",
       "      <td>0.847046</td>\n",
       "      <td>0.814174</td>\n",
       "      <td>0.761494</td>\n",
       "      <td>0.880312</td>\n",
       "      <td>0.759246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR-CRC_species</th>\n",
       "      <td>0.875604</td>\n",
       "      <td>0.937062</td>\n",
       "      <td>0.829861</td>\n",
       "      <td>0.865141</td>\n",
       "      <td>0.799107</td>\n",
       "      <td>0.780172</td>\n",
       "      <td>0.889688</td>\n",
       "      <td>0.764793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT-CRC-2_species</th>\n",
       "      <td>0.872153</td>\n",
       "      <td>0.934434</td>\n",
       "      <td>0.842083</td>\n",
       "      <td>0.86112</td>\n",
       "      <td>0.803571</td>\n",
       "      <td>0.740661</td>\n",
       "      <td>0.879375</td>\n",
       "      <td>0.75943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT-CRC_species</th>\n",
       "      <td>0.883023</td>\n",
       "      <td>0.927803</td>\n",
       "      <td>0.8425</td>\n",
       "      <td>0.860346</td>\n",
       "      <td>0.794643</td>\n",
       "      <td>0.707615</td>\n",
       "      <td>0.865625</td>\n",
       "      <td>0.784578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP-CRC_species</th>\n",
       "      <td>0.851449</td>\n",
       "      <td>0.939189</td>\n",
       "      <td>0.865694</td>\n",
       "      <td>0.865295</td>\n",
       "      <td>0.788504</td>\n",
       "      <td>0.703305</td>\n",
       "      <td>0.869375</td>\n",
       "      <td>0.770895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US-CRC_species</th>\n",
       "      <td>0.876121</td>\n",
       "      <td>0.93506</td>\n",
       "      <td>0.828194</td>\n",
       "      <td>0.868079</td>\n",
       "      <td>0.78404</td>\n",
       "      <td>0.73204</td>\n",
       "      <td>0.874375</td>\n",
       "      <td>0.765163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AT-CRC_species CN-CRC_species DE-CRC_species FR-CRC_species  \\\n",
       "AT-CRC_species         0.883713       0.922297       0.830556       0.859728   \n",
       "CN-CRC_species         0.889924       0.927678       0.823056       0.850758   \n",
       "DE-CRC_species         0.849034       0.930055       0.830694       0.847046   \n",
       "FR-CRC_species         0.875604       0.937062       0.829861       0.865141   \n",
       "IT-CRC-2_species       0.872153       0.934434       0.842083        0.86112   \n",
       "IT-CRC_species         0.883023       0.927803         0.8425       0.860346   \n",
       "JP-CRC_species         0.851449       0.939189       0.865694       0.865295   \n",
       "US-CRC_species         0.876121        0.93506       0.828194       0.868079   \n",
       "\n",
       "                 IT-CRC-2_species IT-CRC_species JP-CRC_species US-CRC_species  \n",
       "AT-CRC_species           0.776228       0.757902       0.828125       0.760725  \n",
       "CN-CRC_species           0.814732       0.707615       0.865313       0.763314  \n",
       "DE-CRC_species           0.814174       0.761494       0.880312       0.759246  \n",
       "FR-CRC_species           0.799107       0.780172       0.889688       0.764793  \n",
       "IT-CRC-2_species         0.803571       0.740661       0.879375        0.75943  \n",
       "IT-CRC_species           0.794643       0.707615       0.865625       0.784578  \n",
       "JP-CRC_species           0.788504       0.703305       0.869375       0.770895  \n",
       "US-CRC_species            0.78404        0.73204       0.874375       0.765163  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUC_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b6feff72",
   "metadata": {},
   "outputs": [],
   "source": [
    "AUC_scores = AUC_scores.apply(pd.to_numeric, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d04d8142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "sns.set_palette(\"husl\", 8)\n",
    "ax = sns.heatmap(AUC_scores, annot = True, cmap=\"crest\", vmin=0.6, vmax=1.0, annot_kws={\"fontsize\":14})\n",
    "ax.set(xlabel=\"Validation Datasets\", ylabel=\"Test Datasets\")\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "plt.savefig(\"plots/LOSO_1.jpg\",dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30cc75f1",
   "metadata": {},
   "source": [
    "# ROC curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e9ecc1a3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fpr</th>\n",
       "      <th>tpr</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AT-CRC_species</th>\n",
       "      <td>0.0,0.0,0.0,0.0,0.0,0.021739130434782608,0.021...</td>\n",
       "      <td>0.0,0.015873015873015872,0.09523809523809523,0...</td>\n",
       "      <td>0.883713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN-CRC_species</th>\n",
       "      <td>0.0,0.0,0.0,0.013513513513513514,0.01351351351...</td>\n",
       "      <td>0.0,0.018518518518518517,0.12962962962962962,0...</td>\n",
       "      <td>0.927678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE-CRC_species</th>\n",
       "      <td>0.0,0.0,0.0,0.016666666666666666,0.01666666666...</td>\n",
       "      <td>0.0,0.03333333333333333,0.06666666666666667,0....</td>\n",
       "      <td>0.830694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR-CRC_species</th>\n",
       "      <td>0.0,0.0,0.0,0.018867924528301886,0.01886792452...</td>\n",
       "      <td>0.0,0.01639344262295082,0.21311475409836064,0....</td>\n",
       "      <td>0.865141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT-CRC-2_species</th>\n",
       "      <td>0.0,0.0,0.0,0.03125,0.03125,0.0625,0.0625,0.09...</td>\n",
       "      <td>0.0,0.03571428571428571,0.14285714285714285,0....</td>\n",
       "      <td>0.803571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT-CRC_species</th>\n",
       "      <td>0.0,0.034482758620689655,0.06896551724137931,0...</td>\n",
       "      <td>0.0,0.0,0.0,0.25,0.25,0.2916666666666667,0.291...</td>\n",
       "      <td>0.707615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP-CRC_species</th>\n",
       "      <td>0.0,0.0,0.0,0.025,0.025,0.05,0.05,0.075,0.075,...</td>\n",
       "      <td>0.0,0.025,0.3,0.325,0.45,0.45,0.5,0.5,0.55,0.7...</td>\n",
       "      <td>0.869375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US-CRC_species</th>\n",
       "      <td>0.0,0.0,0.0,0.0,0.019230769230769232,0.0192307...</td>\n",
       "      <td>0.0,0.019230769230769232,0.09615384615384616,0...</td>\n",
       "      <td>0.765163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                fpr  \\\n",
       "AT-CRC_species    0.0,0.0,0.0,0.0,0.0,0.021739130434782608,0.021...   \n",
       "CN-CRC_species    0.0,0.0,0.0,0.013513513513513514,0.01351351351...   \n",
       "DE-CRC_species    0.0,0.0,0.0,0.016666666666666666,0.01666666666...   \n",
       "FR-CRC_species    0.0,0.0,0.0,0.018867924528301886,0.01886792452...   \n",
       "IT-CRC-2_species  0.0,0.0,0.0,0.03125,0.03125,0.0625,0.0625,0.09...   \n",
       "IT-CRC_species    0.0,0.034482758620689655,0.06896551724137931,0...   \n",
       "JP-CRC_species    0.0,0.0,0.0,0.025,0.025,0.05,0.05,0.075,0.075,...   \n",
       "US-CRC_species    0.0,0.0,0.0,0.0,0.019230769230769232,0.0192307...   \n",
       "\n",
       "                                                                tpr       auc  \n",
       "AT-CRC_species    0.0,0.015873015873015872,0.09523809523809523,0...  0.883713  \n",
       "CN-CRC_species    0.0,0.018518518518518517,0.12962962962962962,0...  0.927678  \n",
       "DE-CRC_species    0.0,0.03333333333333333,0.06666666666666667,0....  0.830694  \n",
       "FR-CRC_species    0.0,0.01639344262295082,0.21311475409836064,0....  0.865141  \n",
       "IT-CRC-2_species  0.0,0.03571428571428571,0.14285714285714285,0....  0.803571  \n",
       "IT-CRC_species    0.0,0.0,0.0,0.25,0.25,0.2916666666666667,0.291...  0.707615  \n",
       "JP-CRC_species    0.0,0.025,0.3,0.325,0.45,0.45,0.5,0.5,0.55,0.7...  0.869375  \n",
       "US-CRC_species    0.0,0.019230769230769232,0.09615384615384616,0...  0.765163  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ROC_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6e4207df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "\n",
    "for i in ROC_curve.index:\n",
    "    plt.plot([float(s) for s in ROC_curve.loc[i]['fpr'].split(\",\")], \n",
    "             [float(s) for s in ROC_curve.loc[i]['tpr'].split(\",\")], \n",
    "             label=\"{}, AUC={:.2f}\".format(i, ROC_curve.loc[i]['auc']))\n",
    "\n",
    "plt.plot([0,1], [0,1], color='orange', linestyle='--')\n",
    "\n",
    "plt.xticks(np.arange(0.0, 1.1, step=0.1))\n",
    "plt.xlabel(\"Flase Positive Rate\")#, fontsize=15)\n",
    "\n",
    "plt.yticks(np.arange(0.0, 1.1, step=0.1))\n",
    "plt.ylabel(\"True Positive Rate\")#, fontsize=15)\n",
    "\n",
    "plt.legend(prop={'size':10}, loc='lower right')\n",
    "plt.savefig(\"plots/LOSO_2.jpg\",dpi=300)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}